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An Environmental Data Collection for COVID-19 Pandemic Research

Author

Listed:
  • Qian Liu

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

  • Wei Liu

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
    College of Land Science and Technology, China Agricultural University, Beijing 100083, China)

  • Dexuan Sha

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

  • Shubham Kumar

    (Dougherty Valley High School, San Ramon, CA 94582, USA)

  • Emily Chang

    (Albemarle High School, Charlottesville, VA 22901, USA)

  • Vishakh Arora

    (Dougherty Valley High School, San Ramon, CA 94582, USA)

  • Hai Lan

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

  • Yun Li

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

  • Zifu Wang

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

  • Yadong Zhang

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China)

  • Zhiran Zhang

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
    School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China)

  • Jackson T. Harris

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
    Department of Geography, Dartmouth College, Hanover, NH 03755, USA)

  • Srikar Chinala

    (University Preparatory Academy, San Jose, CA 95125, USA)

  • Chaowei Yang

    (NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA)

Abstract

The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection.

Suggested Citation

  • Qian Liu & Wei Liu & Dexuan Sha & Shubham Kumar & Emily Chang & Vishakh Arora & Hai Lan & Yun Li & Zifu Wang & Yadong Zhang & Zhiran Zhang & Jackson T. Harris & Srikar Chinala & Chaowei Yang, 2020. "An Environmental Data Collection for COVID-19 Pandemic Research," Data, MDPI, vol. 5(3), pages 1-13, August.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:3:p:68-:d:393767
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    References listed on IDEAS

    as
    1. Heyuan You & Xin Wu & Xuxu Guo, 2020. "Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    2. Beniamino Murgante & Giuseppe Borruso & Ginevra Balletto & Paolo Castiglia & Marco Dettori, 2020. "Why Italy First? Health, Geographical and Planning Aspects of the COVID-19 Outbreak," Sustainability, MDPI, vol. 12(12), pages 1-44, June.
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    Cited by:

    1. Gao, Mingyun & Yang, Honglin & Xiao, Qinzi & Goh, Mark, 2022. "COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    2. van de Water, Antoinette & Henley, Michelle & Bates, Lucy & Slotow, Rob, 2022. "The value of elephants: A pluralist approach," Ecosystem Services, Elsevier, vol. 58(C).
    3. Wang, Xueli & Wang, Lei & Zhang, Xuerong & Fan, Fei, 2022. "The spatiotemporal evolution of COVID-19 in China and its impact on urban economic resilience," China Economic Review, Elsevier, vol. 74(C).

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